The aim of this research is to investigate whether physical gene characteristics can predict age-related changes in gene expression. Specifically, we analyze gene length, GC content, distance to the ends of the chromosome, and similar features to determine their connection with differential expression between young and old individuals. Among these features, gene length consistently shows a strong correlation with age-related expression patterns. However, when combined, the selected features do not provide sufficient predictive power to train a classifier capable of exceeding a modest 66% accuracy. These findings highlight the limitations of the current feature set and point toward the need for more complex feature preprocessing steps or biologically relevant features in future predictive models.

Understanding the mechanisms of aging can help us live longer and healthier lives. Epigenetic age predictors are machine learning models that use methylation levels at CpG sites to predict the biological age of the cell. Horvath’s linear clock uses 353 CpGs with a median absolute error (MedAE) of 3.530, while the deep learning model AltumAge uses 20,318 CpGs to achieve a MedAE of 2.147. This study explores how to improve the accuracy of age predictors through model architecture selection, hyperparameter optimization, and feature selection. ElasticNet regression with recursive feature elimination achieved a MedAE of 2.820 using 341 CpGs, outperforming Horvath’s clock. The two models shared 95 CpG sites, and gene enrichment analysis revealed that several associated genes are involved in stem cell regulation. Feature importance and model interpretation were performed using SHAP analysis, which indicated that age prediction cannot be captured by a small subset of CpG sites. It was concluded that epigenetics has an influence on stem cells, which was found to be a biomarker of aging. Aging remains a complex process that deep learning models may capture better.

Biological aging clocks estimate age from molecular data and provide insights into age-related functional decline. While aging clocks based on bulk transcriptomic data are well-studied, their single-cell counterparts remain limited and underexplored. In this study, we replicate and enhance a recent single-cell RNA-seq aging clock for human immune cells using ElasticNet, improving its performance through refined preprocessing, feature selection, and regularization. We also explore LightGBM to assess nonlinear modeling potential. Our enhanced models reduce prediction error, generalize better across external datasets, and identify biologically relevant genes through SHAP analysis. These findings support the development of accurate, interpretable, cell-type-specific aging clocks using single-cell data.

Aging is the biological process that changes the body over time. When we age our bodies become more prone to disease and other health risks. But not everyone experiences these changes at the same age. This is because the age of our cells (biological age) does not always match our chronological age (time since birth). Being able to predict someone’s biological age and comparing it to their chronological age can be used to infer if someone is indeed more prone to diseases or other health risks.

Other studies have been able to predict the age of cells by using gene expressions. They explore the number of expressions in young and old individuals to identify genes that are affected by age. What has not yet been explored is how the correlation of gene pairs are affected by age. How genes cooperate can change with age, this can be captured by looking at how genes correlate and how that correlation changes with age. This paper will explore these correlations and answer the following question. By performing a correlation analysis between features of young individuals, and on the same features for old individuals, can we interpret any differences and use those to improve current age prediction models?

During this study we found a lot of gene pairs that have a significant difference in correlation from younger to older individuals. We also identified hub genes that change correlation with many other genes. Using these genes to train a linear regression model we were able to predict the age of cells with a Mean Absolute Error of 9.7835.

Using the hub genes we were not able to improve the current existing linear regression model. But we did identify genes that have earlier been linked to aging. Like LIMD2, but also a lot of ribosomal genes and mitochondrial genes, both of which lose functionality with aging.

Cancer represents a huge challenge in the medical world, necessitating early detection methods to improve treatment outcomes. The field of fragmentomics emerged as a promising option towards developing efficient non-invasive cancer diagnosis tools. By analysing the differences between the cfDNA fragments from blood samples of healthy patients and patients with cancer, this study aims to determine the most important fragmentomics features for cancer detection. The methods present in this work involve extracting features from the cfDNA fragments available in the experimental dataset, applying a pipeline of feature selection techniques that removes the redundant features, training and evaluating a logistic regression and random forest classifiers to differentiate between healthy and diseased samples, and finally extracting the feature weights from the trained models to understand which features contributed the most to the classification task. Filter-based variance thresholding and Correlation-based Feature Selection (CFS) were employed to refine the dataset. Independent t-test and the Mann-Whitney U test are used to calculate the relationship between the cancer and healthy samples. The Pearson correlation coefficient calculates the correlation between each pair of features. The classification performance of the two proposed models is assessed using the train/test split and the nested cross-validation techniques. The evaluation reveals that logistic regression constantly outperforms the random forest and that removing the redundant features increases the performance of both classifiers. Certain genomic bins, mostly on chromosomes 1, 7 and 8, contain significant features for the classification task. These findings suggest that understanding the importance of the fragmentomics features can lead to improved diagnostic tools such as cancer detection based on blood tests.

In recent years, a new way of cancer diagnostics has emerged, the analysis of DNA fragments circulating in the blood of cancer patients known as fragmentomics. This DNA, known as cell-free DNA (cfDNA), is an easily available biomarker for cell types. Deducing the tissue origin of cfDNA can reveal anomalies in cell death caused by diseases. That holds great potential in cancer detection and monitoring. The process of establishing the cell composition of a blood sample is called cell deconvolution. This research paper focuses on the comparison of two methods of cell deconvolution. The first one UXM, solves this problem by employing a reference-based technique using a methylation atlas. The second one reference-free cfSort, utilizes a Deep Learning Neural Network to perform the sample analysis. In the paper, however, a simpler architecture was trained due to the difficulties in reproduction. Experiments have been conducted to assess the sensitivity of both methods. Experiments consisted of 5 major cell types together mixed with white blood cell DNA fragments to assess the sensitivity of each method. Furthermore, different metrics such as Pearson's correlation coefficient have been used to determine the accuracy of both methods. In the end, UXM outperformed cfSort in most metrics, including Pearson's correlation coefficient, indicating its superior accuracy in deconvolution tasks. However, cfSort showed potential for higher prediction accuracy with further development and better documentation. The findings highlight the strengths and limitations of both methods. This study suggests that while UXM is currently more reliable, future improvements in cfSort could make it a viable alternative. Continued research is recommended to enhance the accuracy and transparency of these methods, ensuring their effectiveness in real-world healthcare applications.

Detecting cancer at an initial stage could change the course of the disease's development. A non-invasive examination consists of the liquid biopsy of blood, revealing biomarkers that could provide information about the existence of a tumour or not in the organism. The research touches upon the relevance of DNA fragments, precisely the length of fragments, in the detection of cancer. An in-depth interpretation of the fragment length distribution for predicting the state of a patient as being healthy or sick with cancer was approached. The distribution was explored from four perspectives: the complete fragment length distribution, the size range from 90 to 150 bp, important lengths selected by the feature extraction methods and the Fourier Transform of the initial data. These were input in three machine learning models. Using the fragment lengths between 93 and 98 produced accuracy and AUC scores of over 0.85 for all supervised classification models. Processing the data with the Fourier Transform and using the amplitude of spectrums as features in the Random Forest model resulted in an AUC of 0.99.

Recent research has indicated attributes of cell-free DNA (cfDNA) called fragmentomics
as a promising method for late stage cancer detection in a non-invasive manner. The pri-
mary objective of this research is to uncover hidden patterns and interactions that could
enhance the accuracy and sensitivity of blood-based cancer diagnostics. This study explores
the complementarity between three fragmentomics features; fragment length distribution,
and nucleotide fragment end sequence diversity and nucleosome positioning for four dif-
ferent sample groups; breast cancer, colorectal cancer, lung cancer and healthy controls.
Various machine learning techniques such as linear regression were employed to quantify
any complementary relationships between the features

Cancer is one of the leading causes of death. To reduce the amount of deaths caused by cancer, a number of different screening methods are used to detect cancer in an earlier stage, to improve sur vival rates when treating patients with cancer. Cur rent screening methods are often invasive, costly and not very accurate. Therefore, new methods are being sought that aim to be cheaper, less in vasive and provide more accurate results. One of these methods is fragmentomics. Multiple methods have been proposed to use fragmentomics analy sis in the context of screening for cancer, includ ing using the short/long ratio as well as investigat ing the nucleotides at the ends of the fragments. Across previous works using fragmentomics anal ysis to predict cancer, different pre-proccessing steps are used, with limited explanation why the pre-processing methods were chosen. Research into the effects of pre-processing steps used when using fragmentomics analysis is lacking. Two main pre-processing steps in the field are correct ing GC-bias and filtering on MAPQ. Here we in vestigated the impact of three GC-correction meth ods by applying the correction method and then analyzing the resulting fragmentation profiles us ing short/long fragment ratios. Furthermore, three different MAPQ filtering thresholds were studied. This showed that Deeptools correction of the GC bias lowered performance, with the accuracy drop ping from 77.8% to 69.4%. Applying LOESS cor rection using all fragments at the same time re sulted in an accuracy of 83.3%, while applying LOESS correction using the short and long frag ments separately resulted in an accuracy of 91.7%. The impact of filtering the data based on mapping quality was determined by comparing the results of analysing all fragments, analyzing only fragments with mapping quality 5, 20 or 30. This showed that not filtering by mapping quality has a big impact on the profiles of cancer samples, with a KS-test statistic of 0.08 for MAPQ 5 and MAPQ 20 and larger differences in correlations between healthy and cancer samples. The performance of classi fication was much higher when not filtering, with an accuracy of 97.3%, which dropped whenever the filtering threshold was raised, bottoming out at 62.7% for a threshold of MAPQ 30. Due to limita tions with the study, the combined pre-processing of not filtering on MAPQ and using the LOESS separate correction were not studied.